Dataset Information

Quantitative proteomics of breast tumors: Tissue quality assessment to clinical biomarkers.

ABSTRACT: Liquid chromatography-selected reaction monitoring mass spectrometry (LC-SRM) is not only a proven tool for clinical chemistry, but also a versatile method to enhance the capability to quantify biomarkers for tumor biology research. As the treatment of cancer continues to evolve, the ability to assess multiple biomarkers to assign cancer phenotypes based on the genetic background and the signaling of the individual tumor becomes paramount to our ability to treat the patient. In breast cancer, the American Society of Clinical Oncology has defined biomarkers for patient assessment to guide selection of therapy: estrogen receptor, progesterone receptor, and the HER2/Neu receptor tyrosine kinase; therefore, these proteins were selected for LC-SRM assay development. Detailed molecular characterization of these proteins is necessary for patient treatment, so expression and phosphorylation assays have been developed and applied. In addition, other LC-SRM assays were developed to further evaluate tumor biology (e.g. Ki-67 for proliferation and vimentin for tumor aggressiveness related to the epithelial-to-mesenchymal transition). These measurements combined with biomarkers for tissue quality and histological content are implemented in a three-tier multiplexed assay platform, which is translated from cell line models into frozen tumor tissues banked from breast cancer patients.

SUBMITTER: Chen Y

PROVIDER: S-EPMC5996774 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Quantitative proteomics of breast tumors: Tissue quality assessment to clinical biomarkers.

Chen Yi Y Britton David D Wood Elizabeth R ER Brantley Stephen S Magliocco Anthony A Pike Ian I Koomen John M JM

Proteomics 20170301 6

Liquid chromatography-selected reaction monitoring mass spectrometry (LC-SRM) is not only a proven tool for clinical chemistry, but also a versatile method to enhance the capability to quantify biomarkers for tumor biology research. As the treatment of cancer continues to evolve, the ability to assess multiple biomarkers to assign cancer phenotypes based on the genetic background and the signaling of the individual tumor becomes paramount to our ability to treat the patient. In breast cancer, th ...[more]

PMID: 28127872

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Project description:BackgroundBiomarkers are required for pre-symptomatic diagnosis, treatment, and monitoring of neurodegenerative diseases such as Alzheimer's disease. Cerebrospinal fluid (CSF) is a favored source because its proteome reflects the composition of the brain. Ideal biomarkers have low technical and inter-individual variability (subject variance) among control subjects to minimize overlaps between clinical groups. This study evaluates a process of multi-affinity fractionation (MAF) and quantitative label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) for CSF biomarker discovery by (1) identifying reparable sources of technical variability, (2) assessing subject variance and residual technical variability for numerous CSF proteins, and (3) testing its ability to segregate samples on the basis of desired biomarker characteristics.Methods/resultsFourteen aliquots of pooled CSF and two aliquots from six cognitively normal individuals were randomized, enriched for low-abundance proteins by MAF, digested endoproteolytically, randomized again, and analyzed by nano-LC-MS. Nano-LC-MS data were time and m/z aligned across samples for relative peptide quantification. Among 11,433 aligned charge groups, 1360 relatively abundant ones were annotated by MS2, yielding 823 unique peptides. Analyses, including Pearson correlations of annotated LC-MS ion chromatograms, performed for all pairwise sample comparisons, identified several sources of technical variability: i) incomplete MAF and keratins; ii) globally- or segmentally-decreased ion current in isolated LC-MS analyses; and iii) oxidized methionine-containing peptides. Exclusion of these sources yielded 609 peptides representing 81 proteins. Most of these proteins showed very low coefficients of variation (CV<5%) whether they were quantified from the mean of all or only the 2 most-abundant peptides. Unsupervised clustering, using only 24 proteins selected for high subject variance, yielded perfect segregation of pooled and individual samples.ConclusionsQuantitative label-free LC-MS/MS can measure scores of CSF proteins with low technical variability and can segregate samples according to desired criteria. Thus, this technique shows potential for biomarker discovery for neurological diseases.

Dataset Information

Quantitative proteomics of breast tumors: Tissue quality assessment to clinical biomarkers.

Publications

Quantitative proteomics of breast tumors: Tissue quality assessment to clinical biomarkers.

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